One of the major strengths of PET imaging is the capability of measuring tissue radioactivity non-invasively. The measurements of tissue activity are used, along with measurements of blood activity and an appropriate mathematical model for the kinetics of the particular radiotracer, to estimate a number of parameters of interest (e.g. metabolic rate). There are several sources of error in the measurement of tissue activity. Two sources will be studied carefully in this project, both in terms of their magnitude and their impact on parameter estimation: 1) Image noise which arises from the randomness of radioactive decay as it is modified through the process of image reconstruction; and 2) Tissue heterogeneity which is present in all tissues to some extent but is particularly characteristic of tumors. Noise will be measured in a series of PET studies of phantoms simulating the head, thorax, abdomen, and extremities. The noise will be measured over a wide range of count rates and with several different reconstructed image resolutions. The impact of this noise on parameter estimation will be studied for all the models used in the other projects through repetitive optimizations by injecting appropriate noise into simulated data and then determining the parameters using automated parameter optimization. Heterogeneity will be estimated using the method of mixture analysis. This approach assumes each pixel time-activity-curve (TAC) is a linear combination of several sub-TACs, each representative of a homogeneous tissue. Through a process of parameter optimization these sub-TACs are identified and different weights are assigned to each pixel corresponding to the contribution of each sub-TAC to that pixel TAC. The standard deviation of these weights yields an estimate of the degree of heterogeneity. The weighted sum of the parameters for each pixel should yield the appropriate average parameter for each pixel. This method is relatively noise-tolerant and has worked well in preliminary testing. The method of mixture analysis will be carefully examined through a series of studies including: simulations; a physical phantom; and by comparison with heterogeneity measured in a tumor bearing rat imaged with PET and [F-18] fluorodeoxyglucose or [F-18] fluoromisonidazole. This will be compared with an independent measure of heterogeneity in the rat tumor done with quantitative autoradiography following the PET study. Once heterogeneity measurements with mixture analysis are validated. this method will be used to measure heterogeneity in several human tumors. The impact of heterogeneity on parameter estimation will be determined by a series of repetitive parameter optimizations simulating both heterogeneity and noise. A major part of these studies will be to determine the optimum experimental protocols to determine the parameters of interest. We will try to minimize the overall imaging time, the number of blood samples required, and the number of reconstructed images required. This should help simplify the protocols and make them more widely applicable.